01 / REAL
Natural behavior
Unscripted environments and authentic task execution—not staged studio simulations.
Real-world data · Physical AI
The data layer for physical intelligence.
RoboBase captures high-quality human demonstrations across real homes, factories, warehouses and workplaces in China—built to train machines that act in the real world.
Why RoboBase
Robots cannot learn physical work from the internet alone.
Physical intelligence requires seeing how people actually interact with objects, tools and environments. We turn real-world human activity into model-ready datasets—with the scale, diversity and operational rigor that frontier robotics teams need.
How collection works →02 / DIVERSE
Rich variation
Objects, operators, layouts, lighting and edge cases sampled across locations.
03 / PRECISE
Time-aligned signals
Video, pose, motion and task metadata synchronized into a coherent record.
04 / SECURE
Controlled delivery
Consent-led collection, traceable QA and delivery to your exact schema.
Data modalities
From human motion to machine learning signal.
Choose one modality or combine synchronized streams around a precise task definition.
Human demonstration
Egocentric task video
First-person demonstrations of manipulation, assembly, household and operational tasks in real environments.
RGBDepthAudioTask metadata
Spatial motion
Hand & body motion
Structured motion data for understanding contact, intent and fine-grained action.
6DoFHand poseIMU
Manipulation
UMI & teleoperation
Robot-ready demonstrations collected with portable interfaces and controlled task protocols.
ActionsStatesForceVideo
Custom program
Purpose-built datasets
A collection stack designed around your target behavior, sensors, ontology and evaluation needs.
Your schemaYour tasks
Pilot to production
Built around your learning objective.
Start small, inspect real samples, then scale only after the task definition and data quality are proven.
01
Define the task
We translate your model objective into collection instructions, edge cases, sensors and acceptance criteria.
02
Capture a pilot
A small, representative sample validates feasibility, schema and signal quality before scale-up.
03
Review and calibrate
Your team inspects raw and processed data. We refine protocols using concrete feedback.
04
Scale with quality gates
Distributed collection expands under calibrated checks, audit trails and defined delivery milestones.
05
Deliver model-ready data
Structured, versioned datasets arrive in your schema with metadata and quality reports.
China collection network
Real environments. Operational reach.
Access varied homes, factories, warehouses, workshops and commercial spaces through a locally managed collection network. One partner, from field operations through final delivery.
HomesFactoriesWarehousesWorkplacesOutdoors
Start with evidence
Bring us one task.
Share the behavior you want a model or robot to learn. We will shape a focused pilot with representative samples, a clear QA plan and a practical path to scale.
Request a pilot ↗Typical first step: a scoped sample collection and schema review.